实现基于t-SNE坐标，然后用某item的值来体现在颜色上，同时显示基于item值过滤后的密度图

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数据集如下：

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现在用数据集的tSNE_1列作为点的x坐标，tSNE_2列作为点的y坐标，显示HALLMARK_SPERMATOGENESIS列每个点的分数

ggplot2简单定义一个点图叠加密度图的函数

Densityplt2 <- function(data,x,y,score,densitySelect){
    samples = data; X=x ; Y=y ; DensitySelect = densitySelect
    library(ggplot2)
    jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000")) #色盘
    ##核心绘图
    max_expression=max(samples[,Score]) #点对应Score的最大值
    min_expression=min(samples[,Score]) #点对应Score的最小值
    
    arrondi <- function (x) ifelse(x>0, ifelse(x-trunc(x)>=0.5,trunc(x)+1,trunc(x)), ifelse(x-trunc(x)<=-0.5,trunc(x)-1,trunc(x)))
    max_tsneX=arrondi(max(samples[,X]))+1 #设置X轴最大刻度
    min_tsneX=arrondi(min(samples[,X]))-1 #设置X轴最小刻度
    max_tsneY=arrondi(max(samples[,Y]))+1 #设置Y轴最大刻度
    min_tsneY=arrondi(min(samples[,Y]))-1 #设置Y轴最小刻度
    #############
    p <- ggplot(samples, aes_string(x = X, y = Y)) +
        #基于score绘制密度图
        stat_density_2d(data=samples[samples[,Score]>DensitySelect,], aes(fill=..level..,alpha=..level..), geom='polygon', colour='darkgrey', n=50) +
        #绘制点图
        geom_jitter(aes(x = samples[,X], y = samples[,Y], color = samples[,Score]), size=1, alpha=0.7) +
        ###样式调整
        labs(color = "Score") + # color legend
        scale_colour_gradientn(colours = jet.colors(10), limits=c(min_expression, max_expression)) + #点对应的Value映射到颜色
        xlab(X) +
        ylab(Y) +
        theme_bw() +
        theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +  # remove grid
        scale_alpha_continuous(range=c(0.1,0.5))+
        scale_x_continuous(limits = c(min_tsneX, max_tsneX))+ #设置X轴刻度范围
        scale_y_continuous(limits = c(min_tsneY, max_tsneY))+ #设置Y轴刻度范围
        theme(
            plot.title = element_text(size = 14),
            axis.text.x = element_text(size = 14, color='black'),
            axis.text.y = element_text(size = 14, color='black'),
            strip.text.x = element_text(size = 14, color='black',angle=90),
            strip.text.y = element_text(size = 14, color='black',angle=90),
            axis.title.x = element_text(size = 14, color='black'),
            axis.title.y = element_text(size = 14, angle=90))
    return(p)
}

加载数据表(DataFrame):samples <- read.table("*.tsv",sep = '\t',header = T,row.names = 1)
然后调用函数Densityplt2(data = samples,score = "HALLMARK_SPERMATOGENESIS",x = "tSNE_1",y = "tSNE_2",densitySelect = 4)选用数据表的“HALLMARK_SPERMATOGENESIS”列显示样本分数

方法引用自文献:
Pont, Frédéric et al. “Single-Cell Signature Explorer for comprehensive visualization of single cell signatures across scRNA-seq datasets.” Nucleic acids research vol. 47,21 (2019): e133. doi:10.1093/nar/gkz601